Long‐term depression and associativity in rat primary motor cortex following thalamic stimulation
Bibliographic record
Abstract
Associativity is an attractive property of LTP in terms of its possible mechanism as a model for memory storage. In this study, we compare the effects of homosynaptic vs. associative stimulation on the induction of LTP and LTD in the neocortex of freely behaving rats. Using a callosal input to the motor cortex as a 'strong' input (one that potentiates reliably following homosynaptic stimulation), we paired activity of this pathway with a 'weak' thalamocortical pathway (one that does not potentiate when stimulated homosynaptically). Surprisingly, homosynaptic HFS caused a lasting depression of the field EPSP in the thalamocortical pathway. Analysis of this effect revealed that it was largely polysynaptic. Associative HFS (HFS applied to both pathways) not only failed to induce an LTP effect in the thalamocortical pathway, it increased the magnitude of the depression. Associative HFS did, however, facilitate LTP induction in the 'strong' callosal pathway. When comparing the effects of homosynaptic and associative LTD induction (HFS on one pathway anticorrelated with LFS on the other), we found that both protocols induced a similar magnitude of depression. These results show that HFS applied to the thalamocortical pathway causes a depression and this depression is enhanced, not reversed, by associative pairing with a strong input.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".